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Text-mining, pathways, and human disease

Date

November 19, 2009, 6:12 pm
– 9:34 pm

Location

Ryerson Hall, Room 251, The University of Chicago

Description

Institute for Genomics & Systems Biology Seminar Series

2:30 pm to 3:30 pm

Picture a tribe of bright, but ignorant, cave people trying to understand the work of a modern car by analyzing a collection of damaged cars produced by various makers. After many hours of hard manual labor, the cave people disassemble the cars into myriad small parts. Some parts are damaged, whereas some are intact. A few interact with each other, while others do not. Some pieces are different in different cars, yet apparently have the same function. The leap to understanding the whole from knowing the parts requires compilation of many pieces of information into a comprehensive ³computable² model. Researchers in the field of molecular biology are in a situation similar to that of the junkyard cave people, save that they are contemplating a collection of diverse pieces of cellular machinery‹the number of those cellular components is way greater than the number of parts in a typical car‹the number of nodes in human molecular networks is measured in hundreds of thousands when all substances (genes, RNAs, proteins, and other molecules) are considered together. These numerous substances can be in turn present or absent in dozens of cell types in humans‹clearly, the complexity is too great to yield to manual analysis. The information overload in molecular biology is a mere example of the status common to all fields of the current science and culture: An ever-strengthening avalanche of novel data and ideas overwhelms specialists and non-specialists alike, unavoidably fragments knowledge, and makes enormous chunks of knowledge invisible/inaccessible to those who desperately need it. The help of relieving the information overload may come from the text-miners who can automatically extract and catalogue facts described in books and journals. My talk will touch the following six questions: What is text-mining? In what ways is text-mining useful? What can large-scale analyses of scientific literature tell us about both active and forgotten knowledge? What can such analyses tells us about the scientific community itself? How do mathematical models help us to differentiate true and false statements in literature? How will text-mining help us to find cures for human and non-human maladies?

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Persons with a disability who believe they may need assistance should call Liza Herendeen at (773) 834-3913.